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Corruption Perceptions Index

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core

Files Size Format Created Updated License Source
2 59kB csv zip 1 year ago 8 months ago
Corruption perceptions index (cpi) ranks countries/territories in terms of the degree to which corruption is perceived to exist among public officials and politicians. Data the data is sourced from transparency international. > the corruption perceptions index (cpi) ranks countries/territories read more
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Data Files

Download files in this dataset

File Description Size Last changed Download
data 15kB csv (15kB) , json (63kB)
corruption-perceptions-index_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 22kB zip (22kB)

data  

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This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
Jurisdiction 1 string (default)
1998 2 string (default)
1999 3 string (default)
2000 4 string (default)
2001 5 string (default)
2002 6 string (default)
2003 7 string (default)
2004 8 string (default)
2005 9 string (default)
2006 10 string (default)
2007 11 string (default)
2008 12 string (default)
2009 13 string (default)
2010 14 string (default)
2011 15 string (default)
2012 16 string (default)
2013 17 string (default)
2014 18 string (default)
2015 19 string (default)

Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

data get https://datahub.io/core/corruption-perceptions-index
data info core/corruption-perceptions-index
tree core/corruption-perceptions-index
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/corruption-perceptions-index/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/core/corruption-perceptions-index/r/0.csv

curl -L https://datahub.io/core/corruption-perceptions-index/r/1.zip

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")

json_file <- 'https://datahub.io/core/corruption-perceptions-index/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:
print(json_data$resources$name)

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
  if(json_data$resources$datahub$type[i]=='derived/csv'){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))
    print(data)
  }
}

Note: You might need to run the script with root permissions if you are running on Linux machine

Install the Frictionless Data data package library and the pandas itself:

pip install datapackage
pip install pandas

Now you can use the datapackage in the Pandas:

import datapackage
import pandas as pd

data_url = 'https://datahub.io/core/corruption-perceptions-index/datapackage.json'

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('https://datahub.io/core/corruption-perceptions-index/datapackage.json')

# print list of all resources:
print(package.resource_names)

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':
        print(resource.read())

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = 'https://datahub.io/core/corruption-perceptions-index/datapackage.json'

// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
  const dataset = await Dataset.load(path)
  // get list of all resources:
  for (const id in dataset.resources) {
    console.log(dataset.resources[id]._descriptor.name)
  }
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await file.stream()
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data
      stream.pipe(process.stdout)
    }
  }
})()

Read me

Corruption perceptions index (cpi) ranks countries/territories in terms of the degree to which corruption is perceived to exist among public officials and politicians.

Data

the data is sourced from transparency international.

the corruption perceptions index (cpi) ranks countries/territories in terms of the degree to which corruption is perceived to exist among public officials and politicians. it draws on different assessments and business opinion surveys carried out by independent and reputable institutions. it captures information about the administrative and political aspects of corruption. broadly speaking, the surveys and assessments used to compile the index include questions relating to bribery of public officials, kickbacks in public procurement, embezzlement of public funds, and questions that probe the strength and effectiveness of public sector anti-corruption efforts.

more info here.

Preparation

requires:

  1. r - rvest, xlsx
  2. java 8
  3. julia - gadfly, dataframes

note: the scale of the cpi is 0-10 from 1998 to 2011, and 0-100 from 2012 onwards, due to an update to the methodology used to calculate the cpi in 2012.

data in data/ generated by:

$ ./acquire_data.r

or, for the paranoid:

$ torify ./acquire_data.r

acquire_data.r downloads files from transparency international, converts them to csv format, and mergesthem in cpi.csv file.

warning: the files are not at all curated well. many countries are spelled different ways in each annual report, so the scripts will count them as different countries.

License

This Data Package is made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://www.opendatacommons.org/licenses/pddl/1.0/

Datapackage.json

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